Announcements, course schedule, lecture notes, assignments, and grade center will be maintained through Canvas
It is important that you check this page regularly!
Evaluation
Due dates on assignments are posted on Canvas
Homework: 5%
Lecture attendance: 5%
Labs: 10%
Group project: 80%
Group project (real case study): 80%
Team Work Contract: 5%
Group Proposal: 5%
Client Interaction: 10%
Individual written 1st Report: 10%
Individual in-class questions about project: 5%
Midterm Peer Assessment on Group Work: 2.5%
Group Written 2nd Draft: 10%
Group Final Report: 10%
Group Oral Presentation: 10%
Group Poster Session: 10%
Final Peer Assessment on Group Work: 2.5%
Communication
Group communications should be on Slack (join our channel here).
It is important to contribute to the conversations on Slack. If there are complaints about non-responsive group-mates, we will check your Slack history.
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data (Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP)
Data: values of qualitative or quantitative variables that are collected within a particular setting and carry information and knowledge about that setting.
Why Statistics?
Why R?
(And why can’t I just use Excel???)
Recipe Analogy
Using a programming language is like baking with a recipe:
Ingredients = data
Recipe = code
Recipe Results
Someone else can use your recipe (code) to bake the same cake (produce the same data analyses)
What is the goal of STAT450?
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data (Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP).
Students of STAT450 will develop skills to:
understand how the data was collected and its implications in the subsequent analysis
What is the goal of STAT450?
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data (Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP).
Students of STAT450 will develop skills to:
understand how the data was collected and its implications in the subsequent analysis
organize the data in a way that can be analyzed
What is the goal of STAT450?
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data (Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP).
Students of STAT450 will develop skills to:
understand how the data was collected and its implications in the subsequent analysis
organize the data in a way that can be analyzed
analyze the data using appropriate statistical methods to answer the client’s question(s)
What is the goal of STAT450?
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data (Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP).
Students of STAT450 will develop skills to:
understand how the data was collected and its implications in the subsequent analysis
organize the data in a way that can be analyzed
analyze the data using appropriate statistical methods to answer the client’s question(s)
interpret the results
What is the goal of STAT450?
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data (Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP).
Students of STAT450 will develop skills to:
understand how the data was collected and its implications in the subsequent analysis
organize the data in a way that can be analyzed
analyze the data using appropriate statistical methods to answer the client’s question(s)
interpret the results
present and communicate the results
How will this be achieved?
Most course activities will be organized around a case study and reading assignments
Students will participate in:
interactive class discussions
formulation of statistical approaches to solve research problems
data exploration, model building and statistical inference
written and oral presentations of results
Be prepared for a non-typical course!
Communication
Real data, real work, real challenge!!
You will work throughout the course on a real CASE STUDY!
Goal:
Promote collaborative and interdisciplinary work
Give you the opportunity and experience to work on a real case with a real client (something to include in your job portfolio!)
Improve analytical, computational, and communication skills
Project: a real client/collaborator
Case studies will be posted soon – you will get to select your top choices
Based on class discussions, we will form groups and assign a project to each group (3-4 students)
3 meetings with the client during class time (organized by students):
1st meeting: introduction, face to face questions about project, data, and goals
2nd meeting: preliminary results, check if the client has further questions
3rd meeting (Poster Session): final results to the client
Project: group work
Students will work in groups on one assigned case
You will conduct a statistical analysis and write a report using a transparent and reproducible R Markdown report
Group oral presentation, final group report and poster presentation
The first few labs will cover important tools used in the course: Rstudio/Rmarkdown/GitHub
We will use Slack for communication with your project groups - Check out Canvas for the link (expires February 5)!
Academic Concession
Deadline for all assignments is 11:59 pm (Pacific time) on the due date
Any submission or modification after the due date will not be graded unless you have requested an extension
If you anticipate having trouble meeting a deadline and need an academic concession, please reach out in advance via email to the instructors
If you miss class, we suggest you to:
Consult the class resources on Canvas
Use the class Slack workspace to discuss missed material with classmates
Visit office hours
Seek academic concessions, if applicable
Academic Misconduct
Plagiarism occurs where an individual submits or presents the oral or written work of another person or generative Artificial Intelligence (AI) tool as their own.
When words (i.e. phrases, sentences, or paragraphs), ideas, or entire works are taken from elsewhere, their source must be acknowledged. Failure to provide proper attribution is plagiarism.
If you choose to use generative AI tools to complete coursework, you must disclose your use of them. This disclosure must be included at the top of the submission file for the assignment in which the generative AI tool was used. The disclosure should include the name of the tool and a brief description of how it was used.
No client data or specific details of client’s study can be shared with generative AI tools (privacy/intellectual property risk)
You are responsible for verifying the accuracy and correctly attributing any information you take from generative AI output
Academic Integrity (from Learning Commons UBC)
Creating and expressing your own original ideas
Engaging with the ideas of others
Explicitly acknowledging the sources of your knowledge (accurate citation practices)
Completing assignments independently or acknowledging collaboration when appropriate
Accurately reporting the results of your research
Taking exams without cheating
Tips from Learning Commons UBC
When reviewing your work, ask yourself:
Is the idea or argument presented mine?
Are the words my own?
Can my work be clearly distinguished from the work of others?
As many of the projects will involve research that was conducted on humans, everyone will be required to complete an online training module on the Ethical Conduct for Research Involving Humans (see “Homework 0”):
HW0 has already been released and is due Jan 13th
A self-paced online Ethics course – please note this make take several hours to complete so don’t leave it to the last minute!
Data Privacy
You will be working with data from researchers in the community. It is important that these data are stored securely on your (encrypted) computer!
do not upload data to GitHub repositories (servers in US)
no analyses run on public computers
no data in Google Drive/Slack and careful about using Google Doc for reports (consult with client)
What is sensitive data?
For some of the projects you may be working with sensitive data
Some researchers collect data under restricted legal data sharing agreements
Data may contain personal information and/or identifiable information
The researcher has the right to maintain their data private and secure, so make sure to encrypt your computer and follow privacy and information security best practices:
“data analysis presumes the data have already been collected”
“a study includes the development of a hypothesis or question, the designing of the data collection process (or study protocol), the collection of the data, and the analysis and interpretation of the data”
IMPORTANT note: a data analyst needs to understand (or ask) the data collection process!!
“One key indicator of how well your data analysis is going is how easy or difficult it is to match the data you collected to your original expectations”
The first 2 meetings with our clients will help you:
collect the information you need to evaluate/revise expectations
Summary
STAT450 is a non-typical course
We won’t give you a “formula” on how to perform a good data analysis